2017/10/31 by Peiyuan Teng
Computer Science · Materials Science · Mathematics · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Basis (linear algebra) #Computational mechanics #Computer science #Eigenvalues and eigenvectors #Finite element method #Geometry #Machine Learning in Materials Science #Mathematics #Model Reduction and Neural Networks #Monte Carlo method #Neural Networks and Applications #Physics #Quantum Monte Carlo #Quantum mechanics #Radial basis function #Statistical physics #Variational Monte Carlo #Wave function #cond-mat.dis-nn #physics.comp-ph #quant-ph
paper · pdf · doi:10.1103/physreve.98.033305
published as Phys. Rev. E 98, 033305 (2018)
arxiv created 2018/09/11 · openalex publication_date 2018/09/14 · arxiv updated 2018/09/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
In this article, machine-learning methods are used to solve quantum mechanics problems. The radial basis function network in a discrete basis is used as the variational wave function for the ground state of a quantum system. Variational Monte Carlo (VMC) calculations are carried out for some simple Hamiltonians. The results are in good agreement with theoretical values. The smallest eigenvalue of a Hermitian matrix can also be acquired using VMC calculations. Results are provided to demonstrate that machine-learning techniques are capable of solving quantum mechanical problems.